Editorial for the Special Issue “Air Quality Research Using Remote Sensing”
Author:
Abstract
Publisher
MDPI AG
Subject
General Earth and Planetary Sciences
Link
https://www.mdpi.com/2072-4292/14/21/5566/pdf
Reference11 articles.
1. Ibrahim, S., Landa, M., Pešek, O., Brodský, L., and Halounová, L. Machine Learning-Based Approach Using Open Data to Estimate PM2.5 over Europe. Remote Sens., 2022. 14.
2. Qi, L., Zheng, H., Ding, D., Ye, D., and Wang, S. Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM2.5 in China—Implications for PM2.5 Remote Sensing. Remote Sens., 2022. 14.
3. Wang, P., Holloway, T., Bindl, M., Harkey, M., and De Smedt, I. Ambient Formaldehyde over the United States from Ground-Based (AQS) and Satellite (OMI) Observations. Remote Sens., 2022. 14.
4. Liu, Y., He, L., Qin, W., Lin, A., and Yang, Y. The Effect of Urban Form on PM2.5 Concentration: Evidence from China’s 340 Prefecture-Level Cities. Remote Sens., 2022. 14.
5. Ibrahim, S., Landa, M., Pešek, O., Pavelka, K., and Halounova, L. Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe. Remote Sens., 2021. 13.
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